Adaptive filtering techniques are now in widespread use for a number of applications such as adaptive arrays, adaptive line enhancement, adaptive modeling and system identification, adaptive equalization, and adaptive noise cancellation, including acoustic echo cancellation and active noise control.
In particular, the adaptive noise cancellation problem typically involves the generation of a signal which reflects an estimate of a disturbance (i.e., noise) which is to be reduced or eliminated (i.e., cancelled) from a primary source signal. Once determined, this estimate signal may then be subtracted from this primary source signal to reduce the effect of the disturbance. Active noise control in particular involves the generation of a secondary signal (e.g., sound) for the purpose of counteracting the effect of a preexisting noise disturbance. Adaptive filtering techniques are advantageously employed in the context of adaptive noise cancellation because a source signal from which a disturbance has been partially removed may be iteratively tested and processed to further reduce (e.g., minimize) the presence of the disturbance.
Certain adaptive filtering applications involve adaptive filter lengths with hundreds of taps. Examples of such applications include wideband active noise control for complex mechanical structures and acoustic echo cancellation, both of which are characterized by long impulse responses. The computational burden associated with these long adaptive filters precludes their use for many low-cost applications. In addition to computational complexity, adaptive filters with many taps may also suffer from long convergence times, especially if the reference signal spectrum has a large dynamic range.
A technique that involves the use of subbands has been recently exploited to address the above problems. Processing the signals in subbands has a twofold advantage. First, the computational burden is reduced by approximately the number of subbands, since both the tap length and weight update rate can be decimated in each subband. Second, faster convergence is possible because the spectral dynamic range within each subband is greatly reduced as compared to the overall spectral range.
One disadvantage of existing subband adaptive filters, however, is that a delay is necessitated by virtue of the bandpass filters used to derive subband signals. This delay presents a problem for some applications. In active noise control applications, for example, delay seriously limits the bandwidth over which good cancellation can be achieved. For acoustic echo cancellation applications, some transmission systems mandate a very low signal path delay. Thus, conventional subband adaptive filtering techniques may be precluded for applications requiring low delay.